Adaptive Reading Level Assessment for Personalized Search

• US 20170372628A1
• Filed: 07/14/2017
• Published: 12/28/2017
• Est. Priority Date: 02/28/2014
• Status: Abandoned Application

• Current Assignee: Choosito! Inc.
• Original Assignee: Choosito! Inc.
• Inventors: Weiss, David Joseph, Miltsakaki, Eleni
• Application Number:
  US15/650,173
• Publication Number:
  US 20170372628A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G09B 17/00 Teaching reading teaching lip-reading G09B21/00
• CPC Class Codes: G09B 5/02 with visual presentation of the material to be studied, e.g. using film strip
• CPC Class Codes: G09B 7/02 of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student

Abstract

A system and associated methods are provided for generating a representation of the reading ability and general knowledge of a user, receiving information regarding a plurality of electronic documents, generating an estimate of the reading difficulty for the user of each electronic document of the plurality of electronic documents using the generated representation of the reading ability and general knowledge of the user, and presenting results based upon the estimates of the reading difficulty. The representation of the reading ability and general knowledge of a user may then be updated based, in part, upon feedback from the user regarding the presented results.

Claims

1. A computer-implemented method for computing a personalized estimate of reading difficulty for an electronic document, comprising:

generating a representation of the reading ability and general knowledge of a user;

receiving first information regarding a plurality of electronic documents;

generating an estimate of the reading difficulty for the user of each electronic document of the plurality of electronic documents using the generated representation of the reading ability and general knowledge of the user; and

presenting second information regarding the plurality of electronic documents based upon the estimates of the reading difficulty for each of the plurality of electronic documents generated using the representation of the reading ability and general knowledge of the user.

2. The method of claim 1 further comprising:

receiving a search query from the user; and

initiating a search based upon information from the received search query;

wherein the information regarding a plurality of electronic documents is received in response to the search query.

3. The method of claim 1 further comprising:

updating the representation of the reading ability and general knowledge of the user based at least in part upon information provided by the user regarding the presented second information regarding the plurality of electronic documents.

4. The method of claim 1 wherein the first information regarding the plurality of electronic documents comprises links to each of the plurality of electronic documents.

5. The method of claim 1 wherein the second information regarding the plurality of electronic documents comprises links to at least one of the plurality of electronic documents.

6. The method of claim 1 wherein generating the representation of the reading ability and general knowledge of a user comprises:

presenting a plurality of electronic documents to the user via a user interface device;

producing a generic semantic and reading level analysis for each of the presented documents;

obtaining an informational metric by measuring the user'"'"'s implicit and explicit behavior in response to each of the presented documents; and

configuring a computational model of user response based on the user'"'"'s behavior and the semantic and reading level content of the presented documents.

7. The method of claim 1 wherein generating the estimate of the reading difficulty for the user of each electronic document using the generated representation of the reading ability and general knowledge of the user comprises:

producing a generic semantic and reading level analysis of each document; and

producing a user-specific reading difficulty score by applying a computational model of the reading ability and general knowledge of the given user, given the generic semantic and reading level analysis of the document.

8. The method of claim 7 wherein producing a generic reading level analysis comprises:

producing estimates of the probability that the document is associated with each reading level category.

9. The method of claim 7 wherein producing a generic reading level analysis comprises:

determining features including one or more of;

syntactic parses, semantic word associations, word frequencies, analysis of embedded image and video content, properties of hyperlink structure such as the pattern or frequency of hyperlinks.

10. The method of claim 7 wherein producing a generic reading level analysis comprises:

receiving an indication of a generic reading level to be associated with the document from a human annotator.

11. The method of claim 7 wherein producing a generic reading level analysis comprises:

receiving an indication of a generic reading level to be associated with the document from an automatic annotation system.

12. The method of claim 11 wherein the automatic annotation system is trained using a plurality of annotated documents using machine learning software.

13. The method of claim 1 wherein the presented second information are the results of a search query.

14. The method of claim 1 wherein presenting second information regarding the plurality of electronic documents based upon the estimates of the reading difficulty comprises:

filtering or ordering information regarding the electronic documents according to the personalized estimate of reading difficulty.

15. The method of claim 1 wherein generating a representation of the reading ability and general knowledge of a user comprises:

responsive to a determination that informational metrics have not yet been obtained for the given user, initializing the representation from prior estimates using user-specific demographic information.

16. The method of claim 7 wherein producing a generic semantic and reading level analysis of each document comprises:

producing one or more thematic labels for each document.

17. The method of claim 7 wherein producing a generic semantic and reading level analysis of each document comprises:

producing a categorical label indicating the grade level of each document

18. The method of claim 16 wherein producing a generic semantic and reading level analysis of each document comprises:

producing an estimate of the probability that each document contains content for one or more thematic labels.

19. The method of claim 1 wherein generating an estimate of the reading difficulty for the user of each electronic document comprises:

following Bayesian principles to estimate the probability of user response given specific conditions on the reading level and semantic content of the document.

20. The method of claim 6 wherein the informational metric is an explicit response by the user denoting the perceived reading difficulty of a given document.